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How well-structured should your data code be?

The post explores the tradeoff between speed and system reliability, particularly in the context of data science. It delves into the challenges faced by those who prototype ML models, discussing the pressure to move quickly and the decision-making process between handing off work or taking on the role of a Machine Learning Engineer in production.

https://blog.dagworks.io/p/how-well-structured-should-your-data
Python Data Analysis and Visualization Course – Astronomical Data

Learn data analysis, data visualizations, and image processing techniques using real-world astronomical data. The course offers a practical, hands-on approach to simplifying complex concepts in data analysis, making it great for beginners.

https://www.youtube.com/watch?v=H9KefzbryEw
The Scary Thing About Automating Deploys

The article from Slack Engineering discusses the fear of breaking production that holds many teams back from automating their deployments. It emphasizes the importance of understanding how deployment monitoring differs from normal monitoring and the iterative approach taken to ease these fears, ultimately leading to successful automated deployments. The article provides insights into the...

https://slack.engineering/the-scary-thing-about-automating-deploys/
Performance of Vector Databases

The talk explores the role of vector databases in enhancing AI applications like Retrieval-Augmented Generation (RAG), focusing on high-dimensional embeddings crucial for machine learning. Egor Romanov delves into creating a Postgres provider integrated with pgvector, utilizing a Python performance evaluation framework to simulate similarity search tests and unveil latent performance pot...

https://www.youtube.com/watch?v=-MYYB0QjV6I